Robotics and Autonomous Systems 45 (2003) 211–221

Towards selective attention: generating image featuresby learning a visuo-motor map
Takashi Minato∗ , Minoru Asada
Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University,
Yamadaoka 2-1, Suita, Osaka 565-0871, Japan
Received 28 October 2002; received in revised form 11 August 2003; accepted 22 September 2003

Abstract
Robots require a form of visual attention to perform a wide range of tasks effectively. Existing approaches specify inadvance the image features and attention control scheme required for a given robot to perform a specific task. However, tocope with different tasks in a dynamic environment, a robot should be able to construct its own attentional mechanisms. This
paper presents a method that a robot can use to generating image features by learning a visuo-motor map. The robot constructs
the visuo-motor map from training data, and the map constrains both the generation of image features and the estimation ofstate vectors. The resulting image features and state vectors are highly task-oriented. The learned mechanism is attentionalin the sense that it determines what information to select from the image to perform a task. We examine robot experiments
using the proposed method for indoor navigation and scoring soccer goals.
© 2003 Elsevier B.V. All rights reserved.
Keywords: Image feature; Visual attention; Task-oriented; Visual cortex

1. Introduction
Through billions of years of evolution, biologicalsystems have acquired their organs and strategies tosurvive in hostile environments. Visual attention canbe regarded as a combination of such organs andstrategies: vision captures a huge amount of data about
the external world, and attentional mechanisms extract information necessary for the system to achieve
the mission at hand. This capability is desirable for
artificial systems, and it has remained one of themost formidable issues in robotics and AI for many
years.
∗ Corresponding author.
E-mail addresses: minato@ams.eng.osaka-u.ac.jp (T. Minato),asada@ams.eng.osaka-u.ac.jp (M. Asada).

Human beings can readily exploit attentional mechanisms in various kinds of situations, and much research focuses on the early visual processing of humanbeings [8,16,19,20]. Some research applies Shannon’s
information theory to the observed image to select thefocus of attention in the view [14]. The main emphasisof this work is the analysis of human visual processing and the explanation of our own attentional mechanisms.
Some computer vision researchers focused on the
viewpoint selection (i.e., where to look) problem[1,13] in order to disambiguate the descriptions forthe observed image that is obtained by matching theimage with a model database. The selection criterion
is based on the statistics of image data. The actions(gaze control) are intended to obtain a better observation for object recognition, but are not directly related

0921-8890/$ – see front matter © 2003 Elsevier B.V. All rights reserved.
doi:10.1016/j.robot.2003.09.001

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T. Minato, M. Asada / Robotics and Autonomous Systems 45 (2003) 211–221

to the physical actions needed to accomplish a giventask beyond those required for recognition.
Some robot researchers focused on the attention
problem of robot vision. Thrun [15] and Vlassis et al.
[17] extracted image features correlated with the mobile robot’s self-localization information from the observed images based on a probabilistic method. Kröse
and Bunschoten [7] decided the robot direction, thatis, the camera direction, by minimizing the conditional
entropy of the robot position given the observations.
These methods are considered to be task-relevant visual attention but are not related to any physical actions.
As mentioned above, there are many methods toconstruct attentional mechanisms. However, existing
methods do not generally take the robot’s (or human’s)
physical actions into consideration.
Meanwhile, in biological systems, feature extracting cells in the visual cortex develop depending on
visual and motor experiences. The functions of thesecells are not innate, but are adaptively acquired depending on visual experiences in early development
after birth [2,5]. Furthermore, self-produced movement with its concurrent visual feedback is necessary for the development of visuo-motor coordination[4].
In light of these neurophysiological studies, the visual organs of a robot should develop depending on its
visual and motor experiences. Linsker [9,10] showedthat an orientation-selective cell emerged in an artificial multilayered network using modified Hebbianlearning. This result shows that the artificial system
can learn a visual function similar to the one found in
the brain. However, this is a closed system with respect to visual experiences.
In this paper, we focus on extracting image features
as an attentional mechanism, and propose a method forimage feature (e.g., edges or color regions) generation
by visuo-motor map learning depending on the experience gathered by the robot while performing a task.
The training data constructs the visuo-motor mappingthat constrains image feature generation and state vector estimation for the selection of actions. That is, thestate space is constructed so that the correlation between a state and a given instruction can be maximized. The resultant image feature and state vector aretask-oriented. The method is applied to indoor navigation and scoring soccer tasks.

There are some existing methods to construct the visual state spaces through task execution (e.g. [6,12]).
These methods can construct the task-oriented statevector, but they have not focused on image features.
The proposed method constructs the task-oriented visual state space and image features that are useful for
selective attention.
The remainder of the paper is organized as follows.
First, we describe the basic idea of image feature generation along with the learning formulation. We use
the projection matrix from the extracted image feature to the state vector to determine the optimal action.
Next, we give experimental results to show the validity of the proposed method. Finally, we conclude witha discussion on the attentional mechanism suggestedby the current results.
2. Image feature generation
2.1. The basic idea
In the visual cortex, there are many kinds of cells
that extract basic features such as edges from retinal
signals. Higher level processes, such as recognition, are performed according to the cell’s responses (bottom–up signals) and memory or appetite(top–down signals). That is, various kinds of features
are extracted from an input image in a bottom–up
process, and the necessary features are selected according to the task in a top–down process. In ourmodel shown in Fig. 1, we decompose the state estimating process into image feature extraction and state
estimation. The former is similar to the bottom–up
process, while the latter is similar to the top–downprocess. The robot learns the functions to extract theimage features and to estimate the state vector.
The orientation to which the orientation-selective
cells in a kitten’s visual cortex respond adaptively
changes according to its visual experiences during
early development [2,5]. Orientation-selectivity is,however, regarded as innate. Inspired by this biological suggestion, we draw the following analogy: in
our method, using an image filter to extract features
is innate, but the parameters of the image feature arelearned from experience.
The robot generates the filtered image If from theobserved image Io using the filter F , then extracts its

T. Minato, M. Asada / Robotics and Autonomous Systems 45 (2003) 211–221

Observed image Filtered image Compressed image
Io
If
Ic

State

filtering

Actions

Projection matrix
averaging

213

W

compressing

s1
s2

a

filter F.
.
.

nx x ny

sigmoid functiong(x)smnx x ny

image feature generation

nx > ncx
ny > ncy

ncx x ncy

state vector extraction

Fig. 1. Image feature generation and action selection model.

state s from If and decides the action appropriate to
the current state s. To avoid the curse of dimensionality, we reduce the size of the filtered image If tothe compressed image Ic from which the state vector
is estimated by a projection matrix W. Therefore, theproblem is how to learn F and W.
In this two-stage model, we expect the projection
matrix to be more task-oriented than the feature filter. During feature extraction, the interactions between
raw data are limited to local areas, while the connections between the filtered image and the state spreadover the entire space to represent more global interactions. A similar structure can be found in the synaptic
connections of our brain, where the receptive fields inthe retina and V1 of the visual cortex are relatively
narrow and signal transmissions are limited to local
areas while later connections are less localized (e.g.
[3]). We can also regard W as a kind of attentional
mechanism because it connects the compressed image
Ic to the state space, that is, it tells which part in theview is more important to estimate at each state and,
finally, to decide the optimal action.
As mentioned in the last section, the robot should
learn F and W depending on experience gathered
while performing tasks. To learn these task constraints,
we presented to the system a training set composedof positive instances including the robot’s actions andobserved images. F and W are learned so that the
robot can decide which action should correspond tothe observed image. This learning constraint is implemented by minimizing the conditional entropy of theaction given the state.

To learn the coefficients of the filter, we prepare a
3 × 3 spatial filter Fs and a color filter Fc as follows:• a 3 × 3 spatial filter Fs = (fsij ) ∈ R3×3 :Īxy = fs11 Ix−1y−1 + fs12 Ixy−1 + fs13 Ix+1y−1+ fs21 Ix−1y + fs22 Ixy + fs23 Ix+1y+ fs31 Ix−1y+1 + fs32 Ixy+1 + fs33 Ix+1y+1 ,(1)
Ifs xy = g(Īxy ).

(2)• a color filter Fc = (fci ) ∈ R3 :Īxy = fc1 Irxy + fc2 Igxy + fc3 Ibxy ,(3)

Ifc xy = g(Īxy ),(4)

where x and y denote the position of the pixel,
I, Ir , Ig and Ib the gray, red, green and blue components of the observed image, respectively, and g(·)
a sigmoid function. For example, the following Fs
and Fc represent a vertical edge filter and a brightness filter, respectively:−1 0 1
Fs =  −1 0 1  ,
(5)
−1

0


Fc = 0.2990

1
0.58700.1140

T.

(6)

As mentioned above, the robot is given the type offilter Fs or Fc and learns the parameters of the filter
coefficients fsij or fci .

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T. Minato, M. Asada / Robotics and Autonomous Systems 45 (2003) 211–221

2.2. Learning methodp(a’|s)p(a’|s)First, the robot collects supervised successful instances of the given task, and then learns F and W
based on them. In the teaching stage, the robot collects
the ith pair
Ti = Ioi , ai ,(7)

where Io is the observed image, a ∈ Rl is the supervised robot action executed after the robot observes Io
and i denotes the data number.
The state of the robot s ∈ Rm is estimated by W ∈m×n
cx ncy . Let i ∈ R ncx ncy be the one-dimensionalRcrepresentation of Ic , thensj = g((Wic )j ),(8)

where (Wic )j is a jth element of Wic .
To evaluate F and W, we use the conditional entropyof an action a ∈ A given a state s ∈ S:
H(A|S) = −p(s)p(a|s) log p(a|s) da ds,(9)where p(·) is the probabilistic density, A the action
set and S the state set, respectively. To approximate
H(A|S), we use the following function R [17]:
R=−

1
N

N

log p(ai |si ) = −
i

1
N

N

log
i

p(ai , si ),p(si )(10)

where N is the size of the training set. To model p(a, s)
and p(s), we use kernel smoothing [18]:p(s) =

1N

quadratic
approximation

probabilistic
densityaca’(a) Coarse search

a ac

a’

(b) Quadratic interpolation method

Fig. 2. A coarse-to-fine strategy.

p(a|si ) and a unimodal density sharply peaked at a =ai . By minimizing R, we can bring p(a|s) close to
the unimodal density, that is, the robot can uniquely
decide the action a from the state s.
Using the steepest gradient method, we obtain a pairof F and W that minimize R:∂R∂RF ← F − αf,W ← W − αw
,
(15)
∂F
∂W
where αf and αw are the step size parameters.
After learning the robot executes the action a derived from its state s computed from the observed image as follows:a = arg max p(a |s).
a

(16)

To find the maximum value, we adopt a coarse-to-finesearch strategy. At first, we coarsely search approximate maximum density p(ac |s) on the whole action
space (Fig. 2(a)), and then compute maximum density p(a|s) on the proximity space of ac by quadraticinterpolation method (Fig. 2(b)).

N

Ks (s, sq ),(11)

3. Experimentsq1p(a, s) =N

3.1. Tasks and assumptionsN

Ka (a, aq )Ks (s, sq ),

(12)

q

where
Ks (s, sq ) =exp −(2π)m/2 hmss − sq 2
2h2s

,

(13)

Ka (a, aq ) =a − aq 2
1
exp
−
2h2a
(2π)l/2 hla

,

(14)

1

hs and ha are the width of the kernels. R can be
regarded as the Kullback–Leibler distance between

We applied the proposed method to an indoor navigation task with the Nomad mobile robot, Fig. 3(a),and a ball shooting task with a soccer robot, Fig. 3(b).
Although the mobile robot shown in Fig. 3(a) isequipped with stereo cameras, we use only the left
camera image. The soccer robot shown in Fig. 3(b) isequipped with a single camera directed ahead. Eachrobot must move along the given path to the destination using the camera image. The size of the observed
image Io is 64 × 54 pixels and the pixel values ofI, Ir , Ig and Ib are normalized to [0, 1]. The size of

T. Minato, M. Asada / Robotics and Autonomous Systems 45 (2003) 211–221

215

Fig. 3. Task.

the compressed image Ic is 8 × 6 pixels, and eachpixel value is the average value of the corresponding region in If . The robots can execute translational
speed v and steering speed ω independently, so theaction vector is represented asa = (v, ω)T ,(17)

where v and ω are normalized to [−1, 1], respectively.
We defined the dimension of state as m = 2. The
sigmoid function g is
1
g(x) =,
1 + exp(−(x − θ)/c)each time step, the robot collects an instance composedof a given action and an observed image. In each task,
the robot learns a spatial filter Fs and a color filter
Fc , separately. We initialized the components of W tosmall numbers and
0.1 0.1 0.1
Fs =  0.1 0.1 0.1  (smoothing filter), (19)0.1 0.1 0.1

Fc = 0.2990(18)

where θ = 0.0 and c = 0.2.
3.2. Learning results
In the teaching stage, we provided positive instancesby operating the robot to move along the given path. At

0.1140T(filter to extract brightness).

-3.6

-3.8

-3.8

-4

-4
R

-4.2

-4.2

-4.4

-4.4

-4.6
0

2000

4000 6000
update step

8000 10000(20)

3.2.1. Task 1: simple navigation
In the teaching stage, we provided 158 instances.
Fig. 4 shows the changes in R for the Fs and Fc models. R decreased almost monotonically. Fig. 5 shows
the distributions of the state on the training set for themodel with Fs . To visualize the relationship between

-3.6

R

0.5870-4.60(a) Model with Fs

Fig. 4. Learning curves of R.

2000
4000
update step(b) Model with Fc

6000

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T. Minato, M. Asada / Robotics and Autonomous Systems 45 (2003) 211–221
1

1

forwardright turnleft turn0.8

forwardright turnleft turn

0.8

0.6

s2

0.6

0.4

0.4

0.2

0.2

0

0

0.2

0.4

s1

0.6

0.8

1

(a) Initial state space

0

0

0.2

0.4

s1

0.6

0.8

1

(b) Learned state space

Fig. 5. State distributions (task 1, Fs ).

the states and actions, we labeled the action indices asfollows:• v ≥ 0.6 for any ω: forward,• v ≤ −0.6 for any ω: backward,• −0.6 < v < 0.6 and ω < 0.0: right turn, and• −0.6 < v < 0.6 and ω > 0.0: left turn.
The dotted lines in Fig. 5(b) are boundaries of eachclass of actions derived from Eq. (16). As we can see
from these figures, the state space can be roughly classified in terms of actions. That is, the state space isconstructed so that the correlation between classes ofaction and classes of state can be maximized. There-

fore, the robot can almost uniquely decide the actiona from the state s.
The generated Fs and Fc are shown below

−0.8915 −0.5995 −0.06528

1.357  ,Fs =  −0.9696 −0.4790(21)−0.2482 0.1021
2.756

Fc = −0.4233

1.464−0.1718

T

.

(22)

Figs. 6 and 7 show examples of the filtered images.
As we can see from Fig. 6, Fs can extract verticaland horizontal edges. However, Fc does not show any

Fig. 6. An example of the filtered image (Fs ).

Fig. 7. An example of the filtered image (Fc ).

T. Minato, M. Asada / Robotics and Autonomous Systems 45 (2003) 211–221
1

1

fowardright turnbackward
left turn0.8

217
fowardright turnbackward
left turn

0.8

0.6

s2

s2

0.6

0.4

0.4

0.2

0.2

0

0

0.2

0.4

s1

0.6

0.8

1

(a) Initial state space

0

0

0.2

0.4

s1

0.6

0.8

1

(b) Learned state space

Fig. 8. State distributions (task 2, Fc ).

marked characteristics because there are no salient colored objects in the environment (our laboratory). Theedge image extracted by the filter Fs is suitable forrecognizing the scene in the clutter environment, because the robot could discriminate between areas thatwas more textured and areas that was less textured(the floor of the environment).

was 100. The generated Fs and Fc are shown below:
−3.384 −1.953 −1.686

Fs =  0.3491 −1.350 0.5363  ,(23)
1.656 −1.208 5.223

3.2.2. Task 2: shooting a ball
We used the shooting behavior of a soccer robot for
RoboCup as a training set. The number of instances

The state distributions in the case of the model with Fc
are shown in Fig. 8, and the examples of the filteredimage are shown in Figs. 9 and 10.

Fc = 1.836

1.616

Fig. 9. An example of the filtered image (Fs ).

Fig. 10. An example of the filtered image (Fc ).

−4.569

T

.

(24)

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T. Minato, M. Asada / Robotics and Autonomous Systems 45 (2003) 211–221

Fig. 11. A first principal component image.

Fs exhibits the characteristic of extracting horizontal edges (see Fig. 9). Fc emphasizes the red ball andyellow goal but inhibits the white line and wall. Thisis equivalent to a reversed U component in a YUV
image. The red ball and white wall are discriminatedfrom other objects in the image extracted by the filter Fc . Therefore, the generated Fc is suitable for asoccer task in the colored soccer field. The feature
extracted by Fc is not just a statistical characteristic of the environment. To calculate the statistics ofthe color component of the input images, we applied
PCA. Fig. 11(b) shows the first principal componentof color. This is almost entirely the yellow componentof the image, because red (ball), yellow (goal), andgreen (field) are observed with high frequency in thisenvironment. The image extracted by Fc is obviously

suitable for deciding an appropriate action; however,the first principal component image is not. This resultshows that our method reflects not only the statisticalcharacteristics of the observed images, but the actions
needed to accomplish the given task.
3.3. Learned behavior
To verify the validity of the learned model, we applied the model with Fs (task 1) to a navigation task of
the Nomad mobile robot (see Fig. 3(a)). Fig. 12 showsa sequence of the acquired behavior. The observed images in this experiment do not exactly coincide with
the images in the training set, but the robot accomplished the task. Hence, the acquired image feature andstate vector are effective for the task and environment.

Fig. 12. An acquired behavior.

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219

Fig. 13. A projection matrix W (task 1, Fs ).

4. Discussion and future work
We proposed a method to generate an image feature and to learn a projection matrix from the filteredimage to the state that suggests which part of the viewis important, that is, a gaze selection by visuo-motor
mapping. The generated image features are appropriate for the task and environment. Also the acquired
projection matrices give appropriate gaze selection for
the task and environment. To show this, we illustrate
the absolute values of W acquired in the model with
Fs of task 1 and Fc of task 2 in Figs. 13 and 14. Parts(a) and (b) of each figure show the values of components of W to calculate s1 and s2 , respectively. Eachcomponent of W is a weight of the pixel value of Ic . In
these figures, brighter pixels are more closely related
to the state vector, that is, the robot gazes at these partsof its view. Therefore, we can regard that a projection
matrix provides gaze selection.
The model learned by our method tolerates someenvironmental changes depending on the learned filter and projection matrix. For example, the image extracted by Fc in Fig. 10 is not affected by changes inenvironmental illumination. The changes in the par-tially observed image corresponding to the components of W whose values are nearly zero do not affectthe calculation of the state. In general, however, environmental changes require new training data.
In our method, we use kernel smoothing to calculate the probabilities. The number of kernels used inthis method is the same as the number of instances.
Therefore, the cost of calculating R is proportional to
the square of the number of instances. To avoid theincrease in the learning time accompanied by a hugetraining set, we can randomly sample the instances tocalculate the probabilities.
Initially, in this work, we heuristically defined thedimensionality of the state vector to be 2. As a result of experimentation, this dimensionality seems tobe appropriate. Fig. 15(a) shows the relationship between the dimension of the state and the value of R
for learning the model with Fs in task 1, and (b) shows
the relationship between the dimension and the sumof action error. To estimate the action error, we presented the image in the training set to the system andcalculated the error norm between the output action
and the supervised action corresponding to the image. If we evaluate the action error, we can see that

Fig. 14. A projection matrix W (task 2, Fc ).

T. Minato, M. Asada / Robotics and Autonomous Systems 45 (2003) 211–221
-4.4

40

-4.45

30
error

R

220

-4.5
-4.55

20
10

-4.6

0

1

2

3
4
dimension of s
(a) R

5

1

2

3
dimension of s

45(b) Estimation error

Fig. 15. An effect of the dimension of the state vector.

a two-dimensional state is enough for deciding an appropriate robot action.
In the sequence in Fig. 12, there are some cases
where the robot decides the actions with relatively low
probability p(a|s), that is, the robot is not so sure about
its action decision. Therefore, it seems necessary forthe robot to select multiple image features from the image feature set to accomplish more complicated tasks.
Now, we are investigating how to integrate the proposed method and the image feature selection method
based on the information theoretic criterion [11].

5. Conclusion
In this paper, we have proposed a method in whicha robot learns the image feature and state vector thatare effective in the given tasks through its experiences.
It is considered that the brains of mammals including
a human being develop through not only their perception but also the interaction between their bodily
movements and the surrounding environment. Our results suggest that we can draw an analogy between
generating the image features and developing feature
cells in the visual cortex. We hope that our result canbe a clue to studying the development of the brain.
To this end, our next step is to study the learning ofimage features in adapting to environmental changes.
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Takashi Minato received his B.E. and
M.E. degrees in mechanical engineering from Osaka University in 1996 and1998, respectively. He was a researcher
of CREST, JST since December 2001. He
has been a Research Associate of the Department of Adaptive Machine Systems,
Osaka University since September 2002.
He is a member of The Robotics Societyof Japan.

221

Minoru Asada received his B.E., M.E.,
and Ph.D. degrees in control engineering from Osaka University, Osaka, Japan,
in 1977, 1979, and 1982, respectively.
From 1982 to 1988, he was a Research
Associate of Control Engineering, Osaka University, Toyonaka, Osaka, Japan.
Since April 1989, he became an Associate Professor of Mechanical Engineering for Computer-controlled Machinery,
Osaka University, Suita, Osaka, Japan. Since April 1995, he became a Professor of the same department. Since April 1997, hehas been a Professor of the Department of Adaptive Machine Systems at the same university. From August 1986 to October 1987,
he was a visiting researcher of Center for Automation Research,
University of Maryland, College Park, MD.
He has received the 1992 best paper award of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS92),
and the 1996 best paper award of RSJ (Robotics Society of Japan).
He was a general chair of IEEE/RSJ 1996 International Conference on Intelligent Robots and Systems (IROS96). His team was
the first champion team with the USC team in the middle-size
league of the first RoboCup held in conjunction with IJCAI-97.
In 2001, he received a Commendation by the Minister of Education. Since 2002, he has been the President of the International
RoboCup Federation.